MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis

• URL: http://arxiv.org/abs/2602.17901v1
• Date: Thu, 19 Feb 2026 23:45:23 GMT
• Title: MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
• Authors: Junkai Liu, Ling Shao, Le Zhang,
• Abstract summary: We propose MeDUET, a 3D Medical image Disentangled UnifiEd PreTraining framework.<n>MeDUET explicitly disentangles domain-invariant content from domain-specific style.<n>It delivers higher fidelity, faster convergence, and improved controllability for synthesis.
• Score: 36.41448398760502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) and diffusion models have advanced representation learning and image synthesis. However, in 3D medical imaging, they remain separate: diffusion for synthesis, SSL for analysis. Unifying 3D medical image synthesis and analysis is intuitive yet challenging, as multi-center datasets exhibit dominant style shifts, while downstream tasks rely on anatomy, and site-specific style co-varies with anatomy across slices, making factors unreliable without explicit constraints. In this paper, we propose MeDUET, a 3D Medical image Disentangled UnifiEd PreTraining framework that performs SSL in the Variational Autoencoder (VAE) latent space which explicitly disentangles domain-invariant content from domain-specific style. The token demixing mechanism serves to turn disentanglement from a modeling assumption into an empirically identifiable property. Two novel proxy tasks, Mixed-Factor Token Distillation (MFTD) and Swap-invariance Quadruplet Contrast (SiQC), are devised to synergistically enhance disentanglement. Once pretrained, MeDUET is capable of (i) delivering higher fidelity, faster convergence, and improved controllability for synthesis, and (ii) demonstrating strong domain generalization and notable label efficiency for analysis across diverse medical benchmarks. In summary, MeDUET converts multi-source heterogeneity from an obstacle into a learning signal, enabling unified pretraining for 3D medical image synthesis and analysis. The code is available at https://github.com/JK-Liu7/MeDUET .

Related papers

• Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis [9.857855424798732]
  We propose CoPeDiT, a latent diffusion model equipped with completeness perception for unified synthesis of 3D MRIs.<n>CoPeDiT significantly outperforms state-of-the-art methods, achieving superior robustness, generalizability, and flexibility.
  arXiv  Detail & Related papers  (2026-02-20T18:05:39Z)
• M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision [24.846428105192405]
  We train M3Ret, a unified visual encoder, without any modality-specific customization.<n>It successfully learns transferable representations using both generative (MAE) and contrastive (SimDINO) self-supervised learning (SSL) paradigms.<n>Our approach sets a new state-of-the-art in zero-shot image-to-image retrieval across all individual modalities, surpassing strong baselines such as DINOv3 and the text-supervised BMC-CLIP.
  arXiv  Detail & Related papers  (2025-09-01T10:59:39Z)
• Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generation [55.2480439325792]
  Safety-critical applications, such as autonomous driving and medical image analysis, require extensive multimodal data for rigorous testing.<n>This work introduces two novel methods for synthetic data generation in autonomous driving and medical image analysis, namely MObI and AnydoorMed, respectively.
  arXiv  Detail & Related papers  (2025-07-30T19:43:47Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework.<n>Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• A self-supervised framework for learning whole slide representations [52.774822784847565]
  We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of whole slide images. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:05:28Z)
• Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis [35.45013834475523]
  Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Most current medical image synthesis methods rely on generative adversarial networks and suffer from notorious mode collapse and unstable training. We introduce a new paradigm for volumetric medical data synthesis by leveraging 2D backbones and present a diffusion-based framework, Make-A-Volume.
  arXiv  Detail & Related papers  (2023-07-19T16:01:09Z)
• Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis [6.87667643104543]
  Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image analysis tasks. Most current methods follow existing SSL paradigm originally designed for photographic or natural images. We propose a new self-supervised learning framework, namely Alice, that explicitly fulfills Anatomical invariance modeling and semantic alignment.
  arXiv  Detail & Related papers  (2023-02-11T06:36:20Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs [0.5039813366558306]
  We propose a method for generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. We demonstrate that such an approach could provide a solution to diversify and enrich an available database of cardiac MR images.
  arXiv  Detail & Related papers  (2022-09-09T10:17:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.